If the model is composed of differential equations, we may apply numerical integration to compute the state values of the system at any point in time and compare these to the measurements to optimize the model. This removes the requirement that the network should be linear in its parameters. Moreover, an arbitrarily sampled data set can be used. In [1]}, the solutions of differential equations modeled by deep neural networks are computed using a Runge-Kutta scheme. In [2]}, the numerical integration algorithm is directly encoded in the kernels of the Gaussian processes. As an alternative to traditional numerical integration, a neural network can be trained to produce solutions of differential equations [3]}, [4]}. The deep neural network in [5]} is designed to obtain solutions of differential equations and identify model parameters. To the best of our knowledge, no attempts of network sparsification have been made for any of the identification methods that rely on numerical integration.
